Abstract
This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task. The model reconstructs sentences whose word order and word inflections were removed. We divided the problem into two sub-problems: reordering and inflecting. For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules. In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.- Anthology ID:
- D19-6308
- Volume:
- Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Simon Mille, Anja Belz, Bernd Bohnet, Yvette Graham, Leo Wanner
- Venue:
- WS
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 63–67
- Language:
- URL:
- https://aclanthology.org/D19-6308
- DOI:
- 10.18653/v1/D19-6308
- Cite (ACL):
- Farhood Farahnak, Laya Rafiee, Leila Kosseim, and Thomas Fevens. 2019. The Concordia NLG Surface Realizer at SRST 2019. In Proceedings of the 2nd Workshop on Multilingual Surface Realisation (MSR 2019), pages 63–67, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- The Concordia NLG Surface Realizer at SRST 2019 (Farahnak et al., 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/D19-6308.pdf